Biology is the nanotechnology par excellence – 4 Gigayears of evolution have seen to that – and recent work has highlighted the ability of DNA to fold itself into unusual shapes (held together mainly by H-bonds) with interesting machine-like properties – see e.g. recentpapers from the laboratories of Ned Seeman and Milan Stojanovic. DNA aptamers also have interesting and complex binding properties, and I have recently published on a first complete landscape thereof. But it is proteins, with a choice of 20 rather than just 4 building blocks, that give the evolutionary tinkerer or design engineer the greater scope for protein engineering. Nowadays this means not only the engineering of proteins – important in industrial biotechnology – but engineering with proteins, to make interesting and potentially useful structures (with or without catalytic properties) by molecular self-assembly.

One issue in particular in the engineering of structures from parts is how to join their components, and noting the strength of metal-ligand interactions, a series of threepapers highlights the utlility of using metals to staple together suitably engineered proteins. I have long felt that proteins are truly wonderful things – suitable ones can survive boiling yet are at once both flexible and practically incompressible – but we have generally rather neglected their physical properties over their catalytic ones. I was alerted by a tweet to Apple’s licensing of a strong and mouldable ‘liquid metal’ technology that has a useful combination of material properties. Because of the regularity of protein structures one can anticipate that they too might well contribute to novel materials. Needless to say this is not at all a new idea, as a couple of recentreviews from a themed issue of Chem Soc Rev on peptide and protein-based materials demonstrate!

Much of science involves looking for regularities, and clustering objects on the basis of their properties (as in taxonomy) can help us make sense of biological complexity. Comparing a new or unknown thing with what we do know can also give clues to the role of the new thing (‘guilt by association’), and this was an early tool of expression profiling for functional genomics. Of course any clustering algorithm will give some result, but there are now very good means of testing such results for validity. Equally, what we mean by ‘similar’ can have more than one meaning, albeit that we can nowadays explore these simultaneously. As Shakespeare wrote (in Hamlet), “when sorrows come they come not single spies but in battalions”, and a correlation between two variables can mean either that one causes the other or that they both share a separate cause or causes. It is becoming increasingly evident that the greater the likelihood of developing a particular disease, the greater the likelihood of being more susceptible to a different one (e.g. see here). Biological systems, as systems, fail in ways that reflect their construction, and determining how systems fail can tell one much about their organization. To this end, I have been re-reading The Spirit Level, a wonderful book containing some striking correlations together with evidence for causation. Economists take note!

Finally, I enjoyed a metabolomics paper that lit up at least one of the ‘natural’ roles of the rather non-specific organic cation transporter, and as a fan of modeling for understanding complex systems, my attention was drawn by a tweet to another very useful online resource – the Stanford Encyclopedia of Philosophy, on this and other matters.